A panomics-driven framework for the improvement of major food legume crops: advances, challenges, and future prospects

Abstract

Food legume crops, including common bean, faba bean, mungbean, cowpea, chickpea, and pea, have long served as vital sources of energy, protein, and minerals worldwide, both as grains and vegetables. Advancements in high-throughput phenotyping, next-generation sequencing, transcriptomics, proteomics, and metabolomics have significantly expanded genomic resources for food legumes, ushering research into the panomics era. Despite their nutritional and agronomic importance, food legumes still face constraints in yield potential and genetic improvement due to limited genomic resources, complex inheritance patterns, and insufficient exploration of key traits, such as quality and stress resistance. This highlights the need for continued efforts to comprehensively dissect the phenome, genome, and regulome of these crops. This review summarizes recent advances in technological innovations and multi-omics applications in food legumes research and improvement. Given the critical role of germplasm resources and the challenges in applying phenomics to food legumes—such as complex trait architecture and limited standardized methodologies—we first address these foundational areas. We then discuss recent gene discoveries associated with yield stability, seed composition, and stress tolerance and their potential as breeding targets. Considering the growing role of genetic engineering, we provide an update on gene-editing applications in legumes, particularly CRISPR-based approaches for trait enhancement. We advocate for integrating chemical and biochemical signatures of cells (‘molecular phenomics’) with genetic mapping to accelerate gene discovery. We anticipate that combining panomics approaches with advanced breeding technologies will accelerate genetic gains in food legumes, enhancing their productivity, resilience, and contribution to sustainable global food security.

Acknowledgements

The study is financially supported by Shandong Province Key Research and Development Program (2023LZGCQY012), National Natural Science Foundation of China—Youth Science Fund [32402593], Natural Science Foundation of Zhejiang Province [LQN25C150002], National Key R&D Program of China (2023YFD1202702), China Agriculture Research System of MOF and MARA-Food Legumes (CARS-08), and National Natural Science Foundation of China (U24A20419).

Author contributions

H.H., L.N., and X.P. conceptualized and designed the review. H.H., Y.X., S.D.K., Y.T., W.X., F.J., and L.N. wrote the origin draft. H.H., L.Z., S.D.K., and F.J. prepared the figures and tables. W.R., L.L., M.J., and L. Z. conducted literature review. X.P., L.N., and Y.T. revised the manuscript.

Data availability

There are no new data associated with this article.

Conflict of interest statement

The authors declare no competing interests.

References

[1] Lewis G, Schrire B, Mackinder B. et al. Legumes of the World. Kew, UK: Royal Botanical Garden; 2005:592

[2] Nair R, Schreinemachers P. Global status and economic impor-tance of mungbean. In: Nair RM, Schafleitner R, Lee S-H,eds. The Mungbean Genome. Cham: Springer International Publish-ing, 2020,1-8

[3] Uebersax MA, Cichy KA, Gomez FE. et al. Dry beans (Phaseolus vulgaris L.) as a vital component of sustainable agriculture and food security—a review. Legume Sci. 2023; 5:e155

[4] Yan Z, Chu J, Nie J. et al. Legume-based crop diversification with optimal nitrogen fertilization benefits subsequent wheat yield and soil quality. Agric Ecosyst Environ. 2024; 374:109171

[5] Yang X, Xiong J, Du T. et al. Diversifying crop rotation increases food production, reduces net greenhouse gas emissions and improves soil health. Nat Commun. 2024; 15:198

[6] Lobell DB, Burke MB, Tebaldi C. et al.Prioritizing climate change adaptation needs for food security in 2030. Science. 2008; 319: 607-10

[7] Jain A, Sarsaiya S, Singh R. et al. Omics approaches in under-standing the benefits of plant-microbe interactions. Front Micro-biol. 2024; 15:1391059

[8] Saxena KB, Dalvi V, Saxena RK. et al. Hybrid Breeding in Food Legumes with Special Reference to Pigeonpea, Faba bean, and Soybean. In: Saxena KB, Saxena RK, Varshney RK,eds. Genetic Enhancement in Major Food Legumes:Advances in Major Food Legumes. Springer International Publishing: Cham, 2021,123-48

[9] Varshney RK, Pandey MK, Bohra A. et al. Toward the sequence-based breeding in legumes in the post-genome sequencing era. Theor Appl Genet. 2019; 132:797-816

[10] Weckwerth W, Ghatak A, Bellaire A. et al. PANOMICS meets germplasm. Plant Biotechnol J. 2020; 18:1507-25

[11] Tian Z, Nepomuceno AL, Song Q. et al. Soybean2035: a decadal vision for soybean functional genomics and breeding. Mol Plant. 2025; 18:245-71

[12] Sharma S. Prebreeding using wild species for genetic enhance-ment of grain legumes at ICRISAT. Crop Sci. 2017; 57:1132-44

[13] Hyten DL, Song Q, Zhu Y. et al. Impacts of genetic bottlenecks on soybean genome diversity. Proc Natl Acad Sci USA. 2006; 103: 16666-71

[14] Salgotra RK, Chauhan BS. Genetic diversity, conservation, and utilization of plant genetic resources. Genes. 2023; 14:174

[15] Gideon Ladizinsky SA. The Search for Wild Relatives of Cool Season Legumes. Cham: Springer International Publishing; 2015:

[16] Rana JC, Sharma TR, Tyagi RK. et al.Characterisation of 4274 accessions of common bean (Phaseolus vulgaris L.) germplasm conserved in the Indian gene bank for phenological, morpho-logical and agricultural traits. Euphytica. 2015; 205:441-57

[17] Duc G, Bao S, Baum M. et al. Diversity maintenance and use of Vicia faba L. genetic resources. FieldCropRes. 2010; 115:270-8

[18] Upadhyaya HD, Dwivedi SL, Ambrose M. et al. Legume genetic resources: management, diversity assessment, and utilization in crop improvement. Euphytica. 2011; 180:27-47

[19] Commission on Genetic Resources for Food and Agriculture, FAO. The Second Report on the State of the World’s Plant Genetic Resources for Food and Agriculture. Rome: FAO; 2010

[20] Parihar AK, Kumar J, Gupta DS. et al. Genomics enabled breed-ing strategies for major biotic stresses in pea (Pisum sativum l.). Front Plant Sci. 2022; 13:861191

[21] Pandey AK, Jiang L, Moshelion M. et al. Functional physiological phenotyping with functional mapping: a general framework to bridge the phenotype-genotype gap in plant physiology. iScience. 2021; 24:102846

[22] Li Y, Wu X, Xu W. et al. High-throughput physiology-based stress response phenotyping: advantages, applications and prospective in horticultural plants. Hortic Plant J. 2021; 7: 181-7

[23] Houle D, Govindaraju DR, Omholt S. Phenomics: the next chal-lenge. Nat Rev Genet. 2010; 11:855-66

[24] Chen D, Chen M, Altmann T. et al. Bridging Genomics and Phenomics. In: Chen M, Hofestädt R,eds. Approaches in Inte-grative Bioinformatics:Towards the Virtual Cell. Springer Berlin Heidelberg: Berlin, Heidelberg, 2014,299-333

[25] Zhao C, Zhang Y, Du J. et al. Crop phenomics: current status and perspectives. Front Plant Sci. 2019; 10:00714

[26] Bhandari M, Chang A, Jung J. et al. Unmanned aerial system-based high-throughput phenotyping for plant breeding. Plant Phenome J. 2023; 6:e20058

[27] Burridge J, Jochua CN, Bucksch A. et al. Legume shovelomics: high-throughput phenotyping of common bean (and cowpea (Vigna unguiculata subsp, unguiculata) root architecture in the field. Field Crop Res. 2016; 192:21-32

[28] Ubbens JR, Stavness I. Deep plant phenomics: a deep learning platform for complex plant phenotyping tasks. Front Plant Sci. 2017; 8:01190

[29] Pratapa A, Doron M, Caicedo JC.Image-based cell phenotyping with deep learning. Curr Opin Chem Biol. 2021; 65:9-17

[30] van Dijk ADJ, Kootstra G, Kruijer W. et al. Machine learning in plant science and plant breeding. iScience. 2021; 24:101890

[31] Lazarevi ćB, Carović-Stanko K, Živčak M. et al. Classifica-tion of high-throughput phenotyping data for differentiation among nutrient deficiency in common bean. Front Plant Sci. 2022; 13:931877

[32] Wong CY, Gilbert ME, Pierce MA. et al. Hyperspectral remote sensing for phenotyping the physiological drought response of common and tepary bean. Plant Phenomics. 2023; 5:0021

[33] Verheyen J, Dhondt S, Abbeloos R. et al. High-throughput phenotyping reveals multiple drought responses of wild and cultivated Phaseolinae beans. Front Plant Sci. 2024; 15: 1385985

[34] Lippolis A, Polo PV, de Sousa G. et al. High-throughput seed quality analysis in faba bean: leveraging near-infrared spec-troscopy (NIRS) data and statistical methods. Food Chem: X. 2024; 23:101583

[35] Rane J, Raina SK, Govindasamy V. et al. Use of phenomics for differentiation of mungbean (Vigna radiata L. Wilczek) geno-types varying in growth rates per unit of water. Front Plant Sci. 2021; 12:692564

[36] Vadez V, Kholová J, Hummel G. et al. LeasyScan: a novel con-cept combining 3D imaging and lysimetry for high-throughput phenotyping of traits controlling plant water budget. JExp Bot. 2015; 66:5581-93

[37] Fang P, Sun T, Pandey AK. et al. Understanding water con-servation vs. profligation traits in vegetable legumes through a physio-transcriptomic-functional approach. Hortic Res. 2023; 10:uhac287

[38] Pappula-Reddy S-P, Kumar S, Pang J. et al. High-throughput phenotyping for terminal drought stress in chickpea (Cicer arietinum L.). Plant Stress. 2024; 11:100386

[39] Atieno J, Li Y, Langridge P. et al. Exploring genetic variation for salinity tolerance in chickpea using image-based phenotyping. Sci Rep. 2017; 7:1300

[40] Humplík JF, Lazár D, Fürst T. et al. Automated integrative high-throughput phenotyping of plant shoots: a case study of the cold-tolerance of pea (Pisum sativum L.). Plant Methods. 2015; 11:20

[41] Volpato L, Wright EM, Gomez FE. Drone-based digital phe-notyping to evaluating relative maturity, stand count, and plant height in dry beans (Phaseolus vulgaris L.). Plant Phenomics. 2024; 6:0278

[42] Ji Y, Chen Z, Cheng Q. et al. Estimation of plant height and yield based on UAV imagery in faba bean (Vicia faba L.). Plant Methods. 2022; 18:26

[43] Cui Y, Ji Y, Liu R. et al. Faba bean (Vicia faba L.) yield estimation based on dual-sensor data. Drones. 2023; 7:378

[44] Mohammadi S, Uhlen AK, Lillemo M. et al. Enhancing phenotyp-ing efficiency in faba bean breeding: integrating UAV imaging and machine learning. Precis Agric. 2024; 25:1502-28

[45] Ji Y, Liu Z, Cui Y. et al. Faba bean and pea harvest index estimations using aerial-based multimodal data and machine learning algorithms. Plant Physiol. 2024; 194:1512-26

[46] Ji Y, Liu Z, Liu R. et al. High-throughput phenotypic traits estimation of faba bean based on machine learning and drone-based multimodal data. Comput Electron Agric. 2024; 227: 109584

[47] Van Haeften S, Smith D, Robinson H. et al. Unmanned aerial vehicle phenotyping of agronomic and physiological traits in mungbean. Plant Phenome J. 2025; 8:e70016

[48] Zhang C, McGee RJ, Vandemark GJ. et al. Crop performance evaluation of chickpea and dry pea breeding lines across seasons and locations using phenomics data. Front Plant Sci. 2021; 12:640259

[49] Guimarães CM, Stone LF, Zito RK. Susceptibility of common bean and soybean to water stress evaluated at the sitis phe-notyping platform. Biosci J. 2017; 33:871-80

[50] Rascher U, Blossfeld S, Fiorani F. et al. Non-invasive approaches for phenotyping of enhanced performance traits in bean. Funct Plant Biol. 2011; 38:968-83

[51] Ji Y, Liu R, Xiao Y. et al. Faba bean above-ground biomass and bean yield estimation based on consumer-grade unmanned aerial vehicle RGB images and ensemble learning. Precis Agric. 2023; 24:1439-60

[52] Wang J, Liu H, Ren G. Near-infrared spectroscopy (NIRS) evalu-ation and regional analysis of Chinese faba bean (Vicia faba L.). Crop J. 2014; 2:28-37

[53] Johnson JB, Walsh KB, Naiker M. Assessment of bioactive com-pounds in faba bean using infrared spectroscopy. Legume Sci. 2023; 5:e203

[54] Chiteri KO, Chiranjeevi S, Jubery TZ. et al. Dissecting the genetic architecture of leaf morphology traits in mungbean (Vigna radiata (L.) Wizcek) using genome-wide association study. Plant Phenome J. 2023; 6:e20062

[55] Wu X, Sun T, Xu W. et al. Unraveling the genetic architec-ture of two complex, stomata-related drought-responsive traits by high-throughput physiological phenotyping and GWAS in cowpea (Vigna. Unguiculata L. Walp). Front Genet. 2021; 12: 743758

[56] Yu L, Sussman H, Khmelnitsky O. et al. Development of a mobile, high-throughput, and low-cost image-based plant growth phenotyping system. Plant Physiol. 2024; 196:810-29

[57] Lauterberg M, Tschiersch H, Papa R. et al. Engaging preci-sion phenotyping to scrutinize vegetative drought tolerance and recovery in chickpea plant genetic resources. Plan Theory. 2023; 12:2866

[58] Belay AJ, Salau AO, Ashagrie M. et al. Development of a chickpea disease detection and classification model using deep learning. Inform Med Unlocked. 2022; 31:100970

[59] Lauterberg M, Tschiersch H, Zhao Y. et al. Implementation of theoretical non-photochemical quenching (NPQ(T)) to inves-tigate NPQ of chickpea under drought stress with high-throughput phenotyping. Sci Rep. 2024; 14:13970

[60] Sankaran S, Wang M, Vandemark GJ. Image-based rapid phe-notyping of chickpeas seed size. Eng Agric Environ Food. 2016; 9: 50-5

[61] Zhang C, Craine WA, McGee RJ. et al. Image-based pheno-typing of flowering intensity in cool-season crops. Sensors. 2020; 20:1450

[62] Bazrafkan A, Navasca H, Kim J-H. et al. Predicting dry pea matu-rity using machine learning and advanced sensor fusion with unmanned aerial systems (UASs). Remote Sens. 2023; 15:2758

[63] Liu Z, Ji Y, Ya X. et al. Ensemble learning for pea yield estimation using unmanned aerial vehicles, red green blue, and multispec-tral imagery. Drones. 2024; 8:227

[64] Humplík JF, Lazár D, Husičková A. et al. Automated phenotyp-ing of plant shoots using imaging methods for analysis of plant stress responses - a review. Plant Methods. 2015; 11:29

[65] Schmutz J, McClean PE, Mamidi S. et al. A reference genome for common bean and genome-wide analysis of dual domestica-tions. Nat Genet. 2014; 46:707-13

[66] Cortinovis G, Vincenzi L, Anderson R. et al. Adaptive gene loss in the common bean pan-genome during range expansion and domestication. Nat Commun. 2024; 15:6698

[67] Silva DAD, Tsai SM, Chiorato AF. et al. Analysis of the common bean (Phaseolus vulgaris L.) transcriptome regarding efficiency of phosphorus use. PLoS One. 2019; 14:e0210428

[68] Leitão ST, Santos C, Araújo S. et al. Shared and tailored common bean transcriptomic responses to combined fusarium wilt and water deficit. Hortic Res. 2021; 8:149

[69] da Silva HAP, Caetano VS, Pessôa DDV. et al. Unraveling the drought-responsive transcriptomes in nodules of two common bean genotypes during biological nitrogen fixation. Front Plant Sci. 2024; 15:1345379

[70] Subramani M, Urrea CA, Habib R. et al. Comparative transcrip-tome analysis of tolerant and sensitive genotypes of common bean (Phaseolus vulgaris L.) in response to terminal drought stress. Plan Theory. 2023; 12:210

[71] Subramani M, Urrea CA, Tamatamu SR. et al. Comprehensive proteomic analysis of common bean (Phaseolus vulgaris l.) seeds reveal shared and unique proteins involved in terminal drought stress response in tolerant and sensitive genotypes. Biomol Ther. 2024; 14:109

[72] Shiose L, Vidal MS, Heringer AS. et al. Proteomic analysis of common bean (Phaseolus vulgaris L.) leaves showed a more stable metabolism in a variety responsive to biological nitrogen fixation. Symbiosis. 2023; 90:71-80

[73] Padilla-Chacón D, Campos-Patiño L, Peña-Valdivia CB. et al. Proteomic profile of tepary bean seed storage proteins in ger-mination with low water potential. Proteome Sci. 2024; 22:1

[74] Cooper B, Campbell KB, Beard HS. et al. The proteomics of resistance to halo blight in common bean. Mol Plant-Microbe Interact. 2020; 33:1161-75

[75] Kalavacharla V. Understanding histone-DNA interactions in the common bean (Phaseolus vulgaris L.). Epigenetics Chromatin. 2013;6:P37

[76] Kim KD, El Baidouri M, Abernathy B. et al. A comparative epigenomic analysis of polyploidy-derived genes in soybean and common bean. Plant Physiol. 2015; 168:1433-47

[77] Yung W-S, Huang C, Li M-W. et al. Changes in epigenetic features in legumes under abiotic stresses. Plant Genome. 2023; 16:e20237

[78] de Lima F, Boldt ABW, Kava VM. et al. Epigenetics’ role in the common bean (Phaseolus vulgaris L.) and soybean (Glycine max (L.) Merr.) nodulation: a review. Plant Mol Biol Report. 2022; 40: 471-81

[79] Parker TA, Cetz J, de Sousa LL. et al. Loss of pod strings in com-mon bean is associated with gene duplication, retrotransposon insertion and overexpression of PvIND. New Phytol. 2022; 235: 2454-65

[80] Richard MMS, Gratias A, Thareau V. et al. Genomic and epige-nomic immunity in common bean: the unusual features of NB-LRR gene family. DNA Res. 2018; 25:161-72

[81] Wang L, Jia G, Jiang X. et al. Altered chromatin architecture and gene expression during polyploidization and domestication of soybean. Plant Cell. 2021; 33:1430-46

[82] Wu X, Chen S, Zhang Z. et al. A viral small interfering RNA-host plant mRNA pathway modulates virus-induced drought tolerance by enhancing autophagy. Plant Cell. 2024; 36:3219-36

[83] Jayakodi M, Golicz AA, Kreplak J. et al. The giant diploid faba genome unlocks variation in a global protein crop. Nature. 2023; 615:652-9

[84] Zhao N, Zhou E, Miao Y. et al. High-quality faba bean reference transcripts generated using PacBio and Illumina RNA-seq data. Sci Data. 2024; 11:359

[85] Fernández-Aparicio M, Kisugi T, Xie X. et al. Low strigolactone root exudation: a novel mechanism of broomrape (Orobanche and Phelipanche spp.) resistance available for faba bean breed-ing. J Agric Food Chem. 2014; 62:7063-71

[86] Carrillo-Perdomo E, Vidal A, Kreplak J. et al. Development of new genetic resources for faba bean (Vicia faba L.) breeding through the discovery of gene-based SNP markers and the construction of a high-density consensus map. Sci Rep. 2020; 10:6790

[87] Skovbjerg CK, Angra D, Robertson-Shersby-Harvie T. et al. Genetic analysis of global faba bean diversity, agronomic traits and selection signatures. Theor Appl Genet. 2023; 136:114

[88] Khan MA, Alghamdi SS, Ammar MH. et al. Transcriptome profiling of faba bean (Vicia faba L.) drought-tolerant variety hassawi-2 under drought stress using RNA sequencing. Electron J Biotechnol. 2019; 39:15-29

[89] Hou W, Zhang X, Liu Y. et al. RNA-Seq and genetic diversity analysis of faba bean (Vicia faba L.) varieties in China. PeerJ. 2023; 11:e14259

[90] Ocaña S, Seoane P, Bautista R. et al. Large-scale transcriptome analysis in Faba bean (Vicia faba L.) under Ascochyta fabae infection. PLoS One. 2015; 10:e0135143

[91] Braich S, Sudheesh S, Forster JW. et al. Characterisation of faba bean (Vicia faba L.) transcriptome using RNA-Seq: sequencing, de novo assembly, annotation, and expression analysis. Agron-omy. 2017; 7:53

[92] Alghamdi SS, Khan MA, Ammar MH. et al. Characterization of drought stress-responsive root transcriptome of faba bean (Vicia faba L.) using RNA sequencing. 3Biotech. 2018; 8:502

[93] Lyu JI, Ramekar R, Kim JM. et al. Unraveling the complexity of faba bean (Vicia faba L.) transcriptome to reveal cold-stress-responsive genes using long-read isoform sequencing technol-ogy. Sci Rep. 2021; 11:21094

[94] Björnsdotter E, Nadzieja M, Chang W. et al. VC1 catalyses a key step in the biosynthesis of vicine in faba bean. Nat Plants. 2021; 7:923-31

[95] Yang F, Chen H, Liu C. et al. Transcriptome profile analysis of two Vicia faba cultivars with contrasting salinity tolerance during seed germination. Sci Rep. 2020; 10:7250

[96] Yuan X, Wang Q, Yan B. et al. Single-molecule real-time and illumina-based RNA sequencing data identified vernalization-responsive candidate genes in faba bean (Vicia faba L.). Front Genet. 2021; 12:656137

[97] Yang SY, Habili N, Wu Q. et al. Quantitative analysis of pathway enrichment within faba bean seeds RNA-Seq (Vicia faba L). Am JPlant Sci. 2019; 10:2305-34

[98] Shi SH, Lee SS, Zhu YM. et al. Comparative metabolomic pro-filing reveals key secondary metabolites associated with high quality and nutritional value in broad bean (Vicia faba L.). Molecules. 2022; 27:8995

[99] Karolkowski A, Meudec E, Bruguière A. et al. Faba bean (Vicia faba L. minor) bitterness: an untargeted metabolomic approach to highlight the impact of the non-volatile fraction. Meta. 2023; 13:964

[100] Karolkowski A, Belloir C, Martin C. et al. Combining sensory profiling and metabolomic approach to better understand the origins of bitter perception in faba bean (Vicia faba L. minor) fractions. Science Talks. 2024; 11:100379

[101] Elessawy FM, Hughes J, Khazaei H. et al. A comparative metabolomics investigation of flavonoid variation in faba bean flowers. Metabolomics. 2023; 19:52

[102] Zhang C, Ou X, Wang J. et al. Antifungal peptide P852 controls fusarium wilt in faba bean (Vicia faba L.) by promoting antiox-idant defense and isoquinoline alkaloid, betaine, and arginine biosyntheses. Antioxidants. 2022; 11:1767

[103] Somta P, Laosatit K, Yuan X. et al. Thirty years of mung-bean genome research: where do we stand and what have we learned? Front Plant Sci. 2022; 13:944721

[104] Kang YJ, Kim SK, Kim MY. et al. Genome sequence of mungbean and insights into evolution within Vigna species. Nat Commun. 2014; 5:5443

[105] Kim SK, Nair RM, Lee J. et al. Genomic resources in mungbean for future breeding programs. Front Plant Sci. 2015; 6:00626

[106] Ha J, Satyawan D, Jeong H. et al. A near-complete genome sequence of mungbean (Vigna radiata L.) provides key insights into the modern breeding program. Plant Genome. 2021; 14:e20121

[107] Yan Q, Wang Q, Xuzhen C. et al. High-quality genome assembly, annotation and evolutionary analysis of the mungbean (Vigna radiata) genome. Authorea Preprints. 2020

[108] Liu C, Wang Y, Peng J. et al. High-quality genome assembly and pan-genome studies facilitate genetic discovery in mung bean and its improvement. Plant Commun. 2022; 3:100352

[109] Jia K-H, Li G, Wang L. et al. Telomere-to-telomere, gap-free genome of mung bean (Vigna radiata) provides insights into domestication under structural variation. Hortic Res. 2024;;12:uhae337

[110] Liu C, Fan B, Cao Z. et al. A deep sequencing analysis of transcriptomes and the development of EST-SSR markers in mungbean (Vigna radiata). J Genet. 2016; 95:527-35

[111] Sudha M, Karthikeyan A, Madhumitha B. et al. Dynamic tran-scriptome profiling of mungbean genotypes unveil the genes respond to the infection of mungbean yellow mosaic virus. Pathogens. 2022; 11:190

[112] Kumar S, Ayachit G, Sahoo L. Screening of mungbean for drought tolerance and transcriptome profiling between drought-tolerant and susceptible genotype in response to drought stress. Plant Physiol Biochem. 2020; 157:229-38

[113] Kazłowski B, Chen M-R, Chao P-M. et al. Identification and roles of proteins for seed development in mungbean (Vigna radiata L.) seed proteomes. J Agric Food Chem. 2013; 61: 6650-9

[114] Wu X, Wang Y, Tang H. Quantitative metabonomic analy-sis reveals the germination-associated dynamic and systemic biochemical changes for mung-bean (Vigna radiata) seeds. J Proteome Res. 2020; 19:2457-70

[115] Kang YJ, Bae A, Shim S. et al. Genome-wide DNA methylation profile in mungbean. Sci Rep. 2017; 7:40503

[116] Ha J, Kwon H, Cho KH. et al. Identification of epigenetic varia-tion associated with synchronous pod maturity in mungbean (Vigna radiata L.). Sci Rep. 2020; 10:17414

[117] ZhaoP, MaB, Cai C. et al. Transcriptome and methylome changes in two contrasting mungbean genotypes in response to drought stress. BMC Genomics. 2022; 23:80

[118] Lonardi S, Muñoz-Amatriaín M, Liang Q. et al. The genome of cowpea (Vigna unguiculata [L.] Walp.). Plant J. 2019; 98:767-82

[119] Xia Q, Pan L, Zhang R. et al. The genome assembly of aspara-gus bean, Vigna unguiculata ssp. sesquipedialis. Sci Data. 2019; 6:124

[120] Pan L, Liu M, Kang Y. et al. Comprehensive genomic analyses of Vigna unguiculata provide insights into population differen-tiation and the genetic basis of key agricultural traits. Plant Biotechnol J. 2023; 21:1426-39

[121] Liang Q, Muñoz-Amatriaín M, Shu S. et al. A view of the pan-genome of domesticated cowpea (Vigna unguiculata [L.] Walp.). Plant Genome. 2024; 17:e20319

[122] Wu X, Hu Z, Zhang Y. et al. Differential selection of yield and quality traits has shaped genomic signatures of cowpea domestication and improvement. Nat Genet. 2024; 56:992-1005

[123] MacWilliams JR, Nabity PD, Mauck KE. et al. Transcriptome analysis of aphid-resistant and susceptible near isogenic lines reveals candidate resistance genes in cowpea (Vigna unguicu-lata). BMC Plant Biol. 2023; 23:22

[124] Abiala M, Sadhukhan A, Muthuvel J. et al. Rhizosphere Pries-tia species altered cowpea root transcriptome and enhanced growth under drought and nutrient deficiency. Planta. 2022; 257:11

[125] Barrera-Figueroa BE, Gao L, Diop NN. et al. Identification and comparative analysis of drought-associated microRNAs in two cowpea genotypes. BMC Plant Biol. 2011; 11:127

[126] Martins TF, Souza PFN, Alves MS. et al. Identification, charac-terization, and expression analysis of cowpea (Vigna unguiculata [L.] Walp.) miRNAs in response to cowpea severe mosaic virus (CPSMV) challenge. Plant Cell Rep. 2020; 39:1061-78

[127] Paul S, Kundu A, Pal A. Identification and validation of con-served microRNAs along with their differential expression in roots of Vigna unguiculata grown under salt stress. Plant Cell Tissue Organ Cult. 2011; 105:233-42

[128] Bai C, Zhang F, Meng D. et al. Sodium nitroprusside (SNP) generated nitric oxide delays senescence of cowpea (Vigna unguiculata (L.) Walp). Postharvest Biol Technol. Postharvest Biol Technol. 2024; 214:112976

[129] Liu J, Wei L, Zhu L. et al. Integrative transcriptome and metabolome analyses reveal the mechanism of melatonin in delaying postharvest senescence in cowpeas. Int J Biol Macromol. 2024; 282:137429

[130] Zhang S, Yin F, Li J. et al. Transcriptomic and metabolomic investigation of metabolic disruption in Vigna unguiculata L. triggered by acetamiprid and cyromazine. Ecotoxicol Environ Saf. 2022; 239:113675

[131] Tsamo AT, Mohammed H, Mohammed M. et al. Seed coat metabolite profiling of cowpea (Vigna unguiculata L. Walp.) accessions from Ghana using UPLC-PDA-QTOF-MS and chemo-metrics. Nat Prod Res. 2020; 34:1158-62

[132] Li T, Feng M, Chi Y. et al. Defensive resistance of cowpea Vigna unguiculata control Megalurothrips usitatus mediated by jasmonic acid or insect damage. Plan Theory. 2023; 12:942

[133] Han L, Wang Z, Wang Q. et al. Multiomics comprehensive anal-ysis of pre-storage low-temperature on cowpea metabolism. Postharvest Biol Technol. 2024; 216:113056

[134] Varshney RK, Song C, Saxena RK. et al. Draft genome sequence of chickpea (Cicer arietinum) provides a resource for trait improvement. Nat Biotechnol. 2013; 31:240-6

[135] Rehman SA, Gul S, Parthiban M. et al. Genetic resources and genes/QTLs for gram pod borer (Helicoverpa armigera Hübner) resistance in chickpea from the Western Himalayas. Plant Genome. 2024; 17:e20483

[136] Roorkiwal M, Jain A, Kale SM. et al. Development and evaluation of high-density Axiom®Cicer Array for high-resolution genetic mapping and breeding applications in chickpea. Plant Biotechnol J. 2018; 16:890-901

[137] Varshney RK, Roorkiwal M, Sun S. et al. A chickpea genetic variation map based on the sequencing of 3,366 genomes. Nature. 2021; 599:622-7

[138] Khan AW, Garg V, Sun S. et al. Cicer super-pangenome provides insights into species evolution and agronomic trait loci for crop improvement in chickpea. Nat Genet. 2024; 56:1225-34

[139] Negussu M, Karalija E, Vergata C. et al. Drought tolerance mechanisms in chickpea (Cicer arietinum L.) investigated by physiological and transcriptomic analysis. Environ Exp Bot. 2023; 215:105488

[140] Basso MF, Girardin G, Vergata C. et al. Genome-wide tran-script expression analysis reveals major chickpea and lentil genes associated with plant branching. Front Plant Sci. 2024; 15: 1384237

[141] Gupta S, Mishra SK, Misra S. et al. Revealing the complexity of protein abundance in chickpea root under drought-stress using a comparative proteomics approach. Plant Physiol Biochem. 2020; 151:88-102

[142] Vessal S, Arefian M, Siddique KHM. Proteomic responses to progressive dehydration stress in leaves of chickpea seedlings. BMC Genomics. 2020; 21:523

[143] Chaturvedi P, Pierides I, López-Hidalgo C. et al. Natural varia-tion in the chickpea metabolome under drought stress. Plant Biotechnol J. 2024; 22:3278-94

[144] Raman R, Morris S, Sharma N. et al. Metabolite profiling of chickpea (Cicer arietinum) in response to necrotrophic fungus Ascochyta rabiei. Front Plant Sci. 2024; 15:1427688

[145] Singh V, Gupta K, Singh S. et al. Unravelling the molecular mechanism underlying drought stress response in chickpea via integrated multi-omics analysis. Front Plant Sci. 2023; 14: 1156606

[146] Kudapa H, Ghatak A, Barmukh R. et al. Integrated multi-omics analysis reveals drought stress response mechanism in chick-pea (Cicer arietinum L.). Plant Genome. 2024; 17:e20337

[147] Daware A, Mohanty JK, Narnoliya L. et al. Uncovering DNA methylation landscapes to decipher evolutionary footprints of phenotypic diversity in chickpea. DNA Res. 2024;31:dsae013

[148] Pradhan S, Verma S, Chakraborty A. et al. Identification and molecular characterization of miRNAs and their target genes associated with seed development through small RNA sequencing in chickpea. Funct Integr Genom. 2021; 21:283-98

[149] Ucar S, Yaprak E, Yigider E. et al. Genome-wide analysis of miR172-mediated response to heavy metal stress in chickpea (Cicer arietinum L.): physiological, biochemical, and molecular insights. BMC Plant Biol. 2024; 24:1063

[150] Priyadarshini P, Kalwan G, Kohli D. et al. Small RNA sequencing analysis provides novel insights into microRNA-mediated reg-ulation of defense responses in chickpea against Fusarium wilt infection. Planta. 2025; 261:23

[151] Tiwari M, Singh B, Yadav M. et al. High throughput identification of miRNAs reveal novel interacting targets regulating chickpea-rhizobia symbiosis. Environ Exp Bot. 2021; 186:104469

[152] Sindhu A, Ramsay L, Sanderson L-A. et al. Gene-based SNP dis-covery and genetic mapping in pea. Theor Appl Genet. 2014; 127: 2225-41

[153] Pandey AK, Rubiales D, Wang Y. et al. Omics resources and omics-enabled approaches for achieving high productivity and improved quality in pea (Pisum sativum L.). Theor Appl Genet. 2021; 134:755-76

[154] Chen H, Osuna D, Colville L. et al. Transcriptome-wide mapping of pea seed ageing reveals a pivotal role for genes related to oxidative stress and programmed cell death. PLoS One. 2013; 8:e78471

[155] Kreplak J, Madoui M-A, Cápal P. et al. A reference genome for pea provides insight into legume genome evolution. Nat Genet. 2019; 51:1411-22

[156] Yang T, Liu R, Luo Y. et al. Improved pea reference genome and pan-genome highlight genomic features and evolutionary characteristics. Nat Genet. 2022; 54:1553-63

[157] Liu N, Lyu X, Zhang X. et al. Reference genome sequence and population genomic analysis of peas provide insights into the genetic basis of Mendelian and other agronomic traits. Nat Genet. 2024; 56:1964-74

[158] Jan N, Rather AM-U-D, John R. et al. Proteomics for abiotic stresses in legumes: present status and future directions. Crit Rev Biotechnol. 2023; 43:171-90

[159] Mamontova T, Lukasheva E, Mavropolo-Stolyarenko G. et al. Proteome map of pea (Pisum sativum L.) embryos contain-ing different amounts of residual chlorophylls. Int J Mol Sci. 2018; 19:4066

[160] Daba SD, Panda P, Aryal UK. et al. Proteomics analysis of round and wrinkled pea (Pisum sativum L.) seeds during different development periods. Proteomics. 2025; 25:e2300363

[161] Reinprecht Y, Qi Y, Shahmir F. et al. Yield and antiyield genes in common bean (Phaseolus vulgaris L.). Legume Sci. 2021; 3:e91

[162] Xu K, Zhu J, Zhai H. et al. A single-nucleotide polymorphism in PvPW1 encoding β-1,3-glucanase 9 is associated with pod width in Phaseolus vulgaris L. J Genet Genomics. 2024; 51:1413-22

[163] Aragão FJL, Barros LMG, Sousa MV. et al. Expression of a methionine-rich storage albumin from the Brazil nut (Berthol-letia excelsa H.B.K., Lecythidaceae) in transgenic bean plants (Phaseolus vulgaris L., Fabaceae). Genet Mol Biol. 1999; 22:445-9

[164] Astudillo-Reyes C, Fernandez AC, Cichy KA. Transcriptome characterization of developing bean (Phaseolus vulgaris l.) pods from two genotypes with contrasting seed zinc concentrations. PLoS One. 2015; 10:e0137157

[165] de Koning R, Kiekens R, Toili MEM. et al. Identification and expression analysis of the genes involved in the raffinose fam-ily oligosaccharides pathway of Phaseolus vulgaris and Glycine max. Plan Theory. 2021; 10:1465

[166] Yilmaz H, Özer G, Baloch FS. et al. Genome-wide identification and expression analysis of MTP (metal ion transport proteins) genes in the common bean. Plan Theory. 2023; 12:3218

[167] Montanini B, Blaudez D, Jeandroz S. et al. Phylogenetic and func-tional analysis of the cation diffusion facilitator (CDF) family: improved signature and prediction of substrate specificity. BMC Genomics. 2007; 8:107

[168] Perez de Souza L, Scossa F, Proost S. et al. Multi-tissue integra-tion of transcriptomic and specialized metabolite profiling pro-vides tools for assessing the common bean (Phaseolus vulgaris) metabolome. Plant J. 2019; 97:1132-53

[169] Zhao N, Xue D, Miao Y. et al. Construction of a high-density genetic map for faba bean (Vicia faba L.) and quantita-tive trait loci mapping of seed-related traits. Front Plant Sci. 2023; 14:1201103

[170] Ohm H, Åstrand J, Ceplitis A. et al. Novel SNP markers for flowering and seed quality traits in faba bean (Vicia faba L.): characterization and GWAS of a diversity panel. Front Plant Sci. 2024; 15:1348014

[171] Gutierrez N, Pégard M, Solis I. et al. Genome-wide association study for yield-related traits in faba bean (Vicia faba L.). Front Plant Sci. 2024; 15:1328690

[172] Webb A, Cottage A, Wood T. et al. A SNP-based consensus genetic map for synteny-based trait targeting in faba bean (Vicia faba L.). Plant Biotechnol J. 2016; 14:177-85

[173] Gutierrez N, Torres AM. Characterization and diagnostic marker for TTG1 regulating tannin and anthocyanin biosynthe-sis in faba bean. Sci Rep. 2019; 9:16174

[174] Gutierrez N, Avila CM, Torres AM. The bHLH transcription factor VfTT8 underlies zt2, the locus determining zero tannin content in faba bean (Vicia faba L.). Sci Rep. 2020; 10:14299

[175] Liu J, Lin Y, Chen J. et al. Genome-wide association studies provide genetic insights into natural variation of seed-size-related traits in mungbean. Front Plant Sci. 2022; 13:997988

[176] Han X, Li L, Chen H. et al. Resequencing of 558 Chinese mung-bean landraces identifies genetic loci associated with key agro-nomic traits. Front Plant Sci. 2022; 13:1043784

[177] Lin Y, Laosatit K, Liu J. et al. The mungbean VrP locus encod-ing MYB90, an R2R3-type MYB protein, regulates anthocyanin biosynthesis. Front Plant Sci. 2022; 13:895634

[178] Wang Q, Cao H, Wang J. et al. Fine-mapping and primary analysis of candidate genes associated with seed coat color in mung bean (Vigna radiata L.). J Integr Agric. 2024; 23:2571-88

[179] Ma C, Feng Y, Zhou S. et al. Metabolomics and transcriptomics provide insights into the molecular mechanisms of antho-cyanin accumulation in the seed coat of differently colored mung bean (Vigna radiata L.). Plant Physiol Biochem. 2023; 200: 107739

[180] Watcharatpong P, Kaga A, Chen X. et al. Narrowing down a major QTL region conferring pod fiber contents in Yard-long bean (Vigna unguiculata), a vegetable cowpea. Genes. 2020; 11:363

[181] Wu X, Michael VN, López-Hernández F. et al. Genetic diversity and genome-wide association in cowpeas (Vigna unguiculata L. Walp). Agronomy. 2024; 14:961

[182] Lo S, Muñoz-Amatriaín M, Hokin SA. et al. A genome-wide association and meta-analysis reveal regions associated with seed size in cowpea [Vigna unguiculata (L.) Walp]. Theor Appl Genet. 2019; 132:3079-87

[183] Yang Y, Wu Z, Wu Z. et al. A near-complete assembly of aspara-gus bean provides insights into anthocyanin accumulation in pods. Plant Biotechnol J. 2023; 21:2473-89

[184] Li Y, Chen Q, Xie X. et al. Integrated metabolomics and tran-scriptomics analyses reveal the molecular mechanisms under-lying the accumulation of anthocyanins and other flavonoids in cowpea pod (Vigna unguiculata L.). J Agric Food Chem. 2020; 68: 9260-75

[185] Wang R, Gangola MP, Irvine C. et al. Co-localization of genomic regions associated with seed morphology and composition in adesichickpea(Cicer arietinum L.) population varying in seed protein concentration. Theor Appl Genet. 2019; 132:1263-81

[186] Upadhyaya HD, Bajaj D, Narnoliya L. et al. Genome-wide scans for delineation of candidate genes regulating seed-protein con-tent in chickpea. Front Plant Sci. 2016; 7:00302

[187] Chakraborty A, Junaid A, Parida SK. et al. Integrated genomic approaches delineate a novel role of ROP1 ENHANCER1 in controlling seed protein content of chickpea. JExp Bot. 2023; 74: 817-34

[188] Sab S, Lokesha R, Mannur DM. et al. Genome-wide SNP discov-ery and mapping QTLs for seed iron and zinc concentrations in chickpea (Cicer arietinum L.). Front Nutr. 2020; 7:559120

[189] Singh G, Ambreen H, Jain P. et al. Comparative transcriptomic and metabolite profiling reveals genotype-specific responses to Fe starvation in chickpea. Physiol Plant. 2023; 175:e13897

[190] Tan GZH, Das Bhowmik SS, Hoang TML. et al. Investigation of baseline iron levels in Australian chickpea and evalua-tion of a transgenic biofortification approach. Front Plant Sci. 2018; 9:00788

[191] Cheng P, Holdsworth W, Ma Y. et al. Association mapping of agronomic and quality traits in USDA pea single-plant collec-tion. Mol Breed. 2015; 35:75

[192] Gali KK, Sackville A, Tafesse EG. et al. Genome-wide association mapping for agronomic and seed quality traits of field pea (Pisum sativum L.). Front Plant Sci. 2019; 10:01538

[193] Ma Y, Coyne CJ, Grusak MA. et al. Genome-wide SNP identifi-cation, linkage map construction and QTL mapping for seed mineral concentrations and contents in pea (Pisum sativum L.). BMC Plant Biol. 2017; 17:43

[194] Chen B, Shi Y, Sun Y. et al. Innovations in functional genomics and molecular breeding of pea: exploring advances and oppor-tunities. aBIOTECH. 2024; 5:71-93

[195] Rayner T, Moreau C, Ambrose M. et al. Genetic variation con-trolling wrinkled seed phenotypes in Pisum: how lucky was Mendel? Int J Mol Sci. 2017; 18:1205

[196] Clemente A, Arques MC, Dalmais M. et al. Eliminating anti-nutritional plant food proteins: the case of seed protease inhibitors in pea. PLoS One. 2015; 10:e0134634

[197] Zhang P, Wang Y, Sun T. et al. Fine mapping PsPS1, a gene controlling pod softness that defines market type in pea (Pisum sativum). Plant Breed. 2022; 141:418-28

[198] Villordo-Pineda E, González-Chavira MM, Giraldo-Carbajo P. et al. Identification of novel drought-tolerant-associated SNPs in common bean (Phaseolus vulgaris). Front Plant Sci. 2015; 6:00546

[199] Cortés AJ, Blair MW. Genotyping by sequencing and genome-environment associations in wild common bean predict widespread divergent adaptation to drought. Front Plant Sci. 2018; 9:00128

[200] López CM, Pineda M, Alamillo JM. Transcriptomic response to water deficit reveals a crucial role of phosphate acquisition in a drought-tolerant common bean landrace. Plan Theory. 2020; 9:445

[201] Wu L, Chang Y, Wang L. et al. The aquaporin gene PvXIP1;2 conferring drought resistance identified by GWAS at seedling stageincommonbean. Theor Appl Genet. 2022; 135:485-500

[202] Dong X, Zhu H, Hao X. et al. Genome-wide identification of common bean PvLTP family genes and expression profiling analysis in response to drought stress. Genes. 2022; 13:2394

[203] Hiz MC, Canher B, Niron H. et al. Transcriptome analysis of salt tolerant common bean (Phaseolus vulgaris L.) under saline conditions. PLoS One. 2014; 9:e92598

[204] Niron H, Türet M. A putative common bean chalcone O-methyltransferase improves salt tolerance in transgenic Ara-bidopsis thaliana. J Plant Growth Regul. 2020; 39:957-69

[205] González AM, Yuste-Lisbona FJ, Weller J. et al. Characterization of QTL and environmental interactions controlling flowering time in Andean common bean (Phaseolus vulgaris L.). Front Plant Sci. 2021; 11:599462

[206] Vargas Y, Mayor-Duran VM, Buendia HF. et al. Physiological and genetic characterization of heat stress effects in a common bean RIL population. PLoS One. 2021; 16:e0249859

[207] Chavarro MC, Blair MW. QTL analysis and effect of the fin locus on tropical adaptation in an inter-gene pool common bean population. Trop Plant Biol. 2010; 3:204-18

[208] Suárez JC, Polanía JA, Contreras AT. et al. Adaptation of common bean lines to high temperature conditions: genotypic differ-ences in phenological and agronomic performance. Euphytica. 2020; 216:28

[209] Ammar MH, Khan AM, Migdadi HM. et al. Faba bean drought responsive gene identification and validation. Saudi J Biol Sci. 2017; 24:80-9

[210] Huang L-T, Liu C-Y, Li L. et al. Genome-wide identification of bZIP transcription factors in faba bean based on transcrip-tome analysis and investigation of their function in drought response. Plan Theory. 2023; 12:3041

[211] Cao Y-Y, Bian X-C, Chen M-X. et al. iTRAQ-based quantitative proteomic analysis in vernalization-treated faba bean (Vicia faba L.). PLoS One. 2017; 12:e0187436

[212] Maalouf F, Abou-Khater L, Babiker Z. et al. Genetic dissection of heat stress tolerance in faba bean (Vicia faba L.) using GWAS. Plan Theory. 2022; 11:1108

[213] Chang Y, Peng L, Ji L. et al. Genome-wise association study identified genomic regions associated with drought tolerance in mungbean (Vigna radiata (L.)R. Wilczek). Theor Appl Genet. 2023; 136:40

[214] Lin Y, Amkul K, Laosatit K. et al. Fine mapping of QTL conferring resistance to calcareous soil in mungbean reveals VrYSL3 as candidate gene for the resistance. Plant Sci. 2023; 332:111698

[215] Liu J, Xue C, Lin Y. et al. Genetic analysis and identification of VrFRO8, a salt tolerance-related gene in mungbean. Gene. 2022; 836:146658

[216] Breria CM, Hsieh CH, Yen TB. et al. A SNP-based genome-wide association study to mine genetic loci associated to salinity tolerance in mungbean (Vigna radiata L.). Genes. 2020; 11:759

[217] Li S, Liu J, Xue C. et al. Identification and functional characteri-zation of WRKY, PHD and MYB three salt stress responsive gene families in mungbean (Vigna radiata L.). Genes. 2023; 14:463

[218] Wu R, Chen J, Lin Y. et al. Genome-wide identification, expres-sion analysis, and potential roles under abiotic stress of the YUCCA gene family in mungbean (Vigna radiata L.). Int J Mol Sci. 2023; 24:1603

[219] Wu R, Jia Q, Guo Y. et al. Characterization of TBP and TAFs in mungbean (Vigna radiata L.) and their potential involvement in abiotic stress response. Int J Mol Sci. 2024; 25:9558

[220] Li G, Wu X, Hu Y. et al. Orphan genes are involved in drought adaptations and ecoclimatic-oriented selections in domesti-cated cowpea. JExp Bot. 2019; 70:3101-10

[221] Sadhukhan A, Kobayashi Y, Kobayashi Y. et al. VuDREB2A, a novel DREB2-type transcription factor in the drought-tolerant legume cowpea, mediates DRE-dependent expression of stress-responsive genes and confers enhanced drought resistance in transgenic Arabidopsis. Planta. 2014; 240:645-64

[222] Chankaew S, Isemura T, Naito K. et al. QTL mapping for salt tolerance and domestication-related traits in Vigna marina subsp. oblonga, a halophytic species. Theor Appl Genet. 2014; 127: 691-702

[223] Ravelombola W, Shi A, Weng Y. et al. Association analysis of salt tolerance in cowpea (Vigna unguiculata (L.) Walp) at germination and seedling stages. Theor Appl Genet. 2018; 131:79-91

[224] Ravelombola W, Shi A, Huynh B-L. et al. Genetic architecture of salt tolerance in a Multi-Parent Advanced Generation Inter-Cross (MAGIC) cowpea population. BMC Genomics. 2022; 23:100

[225] PanL, YuX, ShaoJ. et al. Transcriptomic profiling and analysis of differentially expressed genes in asparagus bean (Vigna unguiculata ssp. sesquipedalis) under salt stress. PLoS One. 2019; 14:e0219799

[226] Srivastava R, Kobayashi Y, Koyama H. et al. Cowpea NAC1/NAC2 transcription factors improve growth and tolerance to drought and heat in transgenic cowpea through combined activation of photosynthetic and antioxidant mechanisms. J Integr Plant Biol. 2023; 65:25-44

[227] Mishra S, Sahu G, Shaw BP. Insight into the cellular and phys-iological regulatory modulations of class-I TCP9 to enhance drought and salinity stress tolerance in cowpea. Physiol Plant. 2022; 174:e13542

[228] Liang L, Sui X, Xiao J. et al. ERD14 regulation by the HY5- or HY5-MED2 module mediates the cold signal transduction of asparagus bean. Plant J. 2025; 121:e17172

[229] Liang L, Wang D, Xu D. et al. Comparative phylogenetic analysis of the mediator complex subunit in asparagus bean (Vigna unguiculata ssp. sesquipedialis) and its expression profile under cold stress. BMC Genomics. 2024; 25:149

[230] Yadava YK, Chaudhary P, Yadav S. et al. Genetic mapping of quantitative trait loci associated with drought tolerance in chickpea (Cicer arietinum L.). Sci Rep. 2023; 13:17623

[231] ThudiM, SamineniS, LiW. et al. Whole genome resequenc-ing and phenotyping of MAGIC population for high resolu-tion mapping of drought tolerance in chickpea. Plant Genome. 2024; 17:e20333

[232] Istanbuli T, Nassar AE, Abd El-Maksoud MM. et al. Genome-wide association study reveals SNP markers controlling drought tolerance and related agronomic traits in chickpea across multiple environments. Front Plant Sci. 2024; 15:1260690

[233] Ahmed SM, Alsamman AM, Jighly A. et al. Genome-wide asso-ciation analysis of chickpea germplasms differing for salin-ity tolerance based on DArTseq markers. PLoS One. 2021; 16: e0260709

[234] Mohanty JK, Thakro V, Yadav A. et al. Delineation of genes for a major QTL governing heat stress tolerance in chickpea. Plant Mol Biol. 2024; 114:19

[235] Mohanty JK, Yadav A, Narnoliya L. et al. A next-generation combinatorial genomic strategy scans genomic loci governing heat stress tolerance in chickpea. Plant Cell Environ. 2025; 48: 2706-26

[236] Danakumara T, Kumar N, Patil BS. et al. Unraveling the genet-ics of heat tolerance in chickpea landraces (Cicer arietinum L.) using genome-wide association studies. Front Plant Sci. 2024; 15:1376381

[237] Panigrahi S, Kumar U, Swami S. et al. Meta QTL analysis for dissecting abiotic stress tolerance in chickpea. BMC Genomics. 2024; 25:439

[238] Farooq M, Ahmad R, Shahzad M. et al. Real-time expression and in silico characterization of pea genes involved in salt and water-deficit stress. MolBiolRep. 2023; 51:18

[239] Sahoo RK, Chandan RK, Swain DM. et al. Heterologous over-expression of PDH45 gene of pea provides tolerance against sheath blight disease and drought stress in rice. Plant Physiol Biochem. 2022; 186:242-51

[240] Shivakumara TN, Sreevathsa R, Dash PK. et al. Overexpres-sion of pea DNA helicase 45 (PDH45) imparts tolerance to multiple abiotic stresses in chili (Capsicum annuum L.). Sci Rep. 2017; 7:2760

[241] Yuan H, Liu B, Zhang G. et al. Genome-wide identification and expression analysis of the PsKIN gene family in pea. Front Genet. 2024; 15:1510864

[242] Jovanovic Z, Stanisavljevic N, Mikic A. et al. The expression of drought responsive element binding protein (DREB2A) related gene from pea (Pisum sativum L.) as affected by water stress. Aust J Crop Sci. 2013; 7:1590-6

[243] Moazzam-Jazi M, Ghasemi S, Seyedi SM. et al. COP1 plays a prominent role in drought stress tolerance in Arabidopsis and pea. Plant Physiol Biochem. 2018; 130:678-91

[244] Jha UC, Bohra A, Jha R. et al. Salinity stress response and ‘omics’ approaches for improving salinity stress tolerance in major grain legumes. Plant Cell Rep. 2019; 38:255-77

[245] Vaid N, Pandey P, Srivastava VK. et al. Pea lectin receptor-like kinase functions in salinity adaptation without yield penalty, by alleviating osmotic and ionic stresses and upregulating stress-responsive genes. Plant Mol Biol. 2015; 88:193-206

[246] Passricha N, Saifi SK, Kharb P. et al. Marker-free transgenic rice plant overexpressing pea LecRLK imparts salinity tolerance by inhibiting sodium accumulation. Plant Mol Biol. 2019; 99: 265-81

[247] Banu MSA, Huda KMK, Sahoo RK. et al. Pea p68 imparts salinity stress tolerance in rice by scavenging of ROS-mediated H2O2 and interacts with argonaute. Plant Mol Biol Report. 2015; 33: 221-38

[248] Jain P, Singh S, Sinha S. et al. Chapter 17 - Common bean disease improvement using QTL mapping. In: Wani SH, Wang D, Pratap Singh G,eds. QTL Mapping in Crop Improvement. New York,USA: Academic Press; 2023:355-76

[249] Liu H, Wang D, Wang Z. et al. Identification of MAPK genes in Phaseolus vulgaris and analysis of their expression patterns in response to anthracnose. Int J Mol Sci. 2024; 25:13101

[250] Shafi S, Tahir M, Bawa V. et al. Biochemical defense arse-nal, genes/QTLs and transcripts for imparting anthracnose resistance in common bean (Phaseolus vulgaris L.). Plant Stress. 2024; 14:100609

[251] XueR, FengM, ChenJ. et al. A methyl esterase 1 (PvMES1) promotes the salicylic acid pathway and enhances Fusarium wilt resistance in common beans. Theor Appl Genet. 2021; 134: 2379-98

[252] Liu Y, Huang Y, Li Z. et al. Genome-wide identification of the TGA genes in common bean (Phaseolus vulgaris) and revealing their functions in response to Fusarium oxysporum f. sp. phaseoli infection. Front Genet. 2023; 14:1137634

[253] Abdelkhalek A, Bashir S, El-Gendi H. et al. Protective activity of Rhizobium leguminosarum bv. Viciae strain 33504-mat209 against alfalfa mosaic virus infection in faba bean plants. Plan Theory. 2023; 12:2658

[254] Rubiales D, Rojas-Molina MM, Sillero JC.Characterization of resistance mechanisms in faba bean (Vicia faba) against broom-rape species (Orobanche and Phelipanche spp.). Front Plant Sci. 2016; 7:1747

[255] Fernández-Aparicio M, Yoneyama K, Rubiales D. The role of strigolactones in host specificity of Orobanche and Phelipanche seed germination. Seed Sci Res. 2011; 21:55-61

[256] Lin W-J, Ko C-Y, Liu M-S. et al. Transcriptomic and proteomic research to explore bruchid-resistant genes in mungbean iso-genic lines. J Agric Food Chem. 2016; 64:6648-58

[257] Liu M-S, Kuo TC-Y, Ko C-Y. et al. Genomic and transcriptomic comparison of nucleotide variations for insights into bruchid resistance of mungbean (Vigna radiata [L.] R. Wilczek). BMC Plant Biol. 2016; 16:46

[258] Chotechung S, Somta P, Chen J. et al. Ageneencodinga polygalacturonase-inhibiting protein (PGIP) is a candidate gene for bruchid (Coleoptera: bruchidae) resistance in mungbean (Vigna radiata). Theor Appl Genet. 2016; 129:1673-83

[259] Kaewwongwal A, Liu C, Somta P. et al. A second VrPGIP1 allele is associated with bruchid resistance (Callosobruchus spp.) in wild mungbean (Vigna radiata var. sublobata) accession ACC41. Mol Gen Genomics. 2020; 295:275-86

[260] Wu R, Zhang Q, Lin Y. et al. Marker-assisted backcross breeding for improving bruchid (Callosobruchus spp.) resistance in mung bean (Vigna radiata L.). Agronomy. Agronomy. 2022; 12:1271

[261] Kohli M, Bansal H, Mishra GP. et al. Genome-wide association studies for earliness, MYMIV resistance, and other associated traits in mungbean (Vigna radiata L. Wilczek) using genotyping by sequencing approach. PeerJ. 2024; 12:e16653

[262] Dhaliwal SK, Gill RK, Sharma A. et al. A large-effect QTL intro-gressed from ricebean imparts resistance to Mungbean yellow mosaic India virus in blackgram (Vigna mungo (L.) Hepper). Theor Appl Genet. 2022; 135:4495-506

[263] Chankaew S, Somta P, Isemura T. et al. Quantitative trait locus mapping reveals conservation of major and minor loci for powdery mildew resistance in four sources of resistance in mungbean [Vigna radiata (L.) Wilczek]. Mol Breed. 2013; 32:121-30

[264] Yundaeng C, Somta P, Chen J. et al. Wilczek). Plant Sci. 2020; 298:110594

[265] Waengwan P, Laosatit K, Lin Y. et al. A cluster of peronospora parasitica 13-like (NBS-LRR) genes is associated with powdery mildew (Erysiphe polygoni) resistance in mungbean (Vigna radi-ata). Plan Theory. 2024; 13:1230

[266] Yundaeng C, Somta P, Chen J. et al. Fine mapping of QTL con-ferring Cercospora leaf spot disease resistance in mungbean revealed TAF5 as candidate gene for the resistance. Theor Appl Genet. 2021; 134:701-14

[267] Wu X, Li G, Wang B. et al. Fine mapping Ruv2, a new rust resistance gene in cowpea (Vigna unguiculata), to a 193-kb region enriched with NBS-type genes. Theor Appl Genet. 2018; 131: 2709-18

[268] Heng T, Kaga A, Chen X. et al. Two tightly linked genes coding for NAD-dependent malic enzyme and dynamin-related protein are associated with resistance to Cercospora leaf spot dis-ease in cowpea (Vigna unguiculata (L.) Walp.). Theor Appl Genet. 2020; 133:395-407

[269] Alsamman AM, Mousa KH, Istanbuli T. et al. Unveiling the genetic basis of Fusarium wilt resistance in chickpea using GWAS analysis and characterization of candidate genes. Front Genet. 2024; 14:1292009

[270] Chakraborty J, Ghosh P, Sen S. et al. CaMPK9 increases the sta-bility of CaWRKY40 transcription factor which triggers defense response in chickpea upon Fusarium oxysporum f. sp. ciceri Race1 infection. Plant Mol Biol. 2019; 100:411-31

[271] Kumar K, Purayannur S, Kaladhar VC. et al. mQTL-seq and clas-sical mapping implicates the role of an AT-HOOK MOTIF CON-TAINING NUCLEAR LOCALIZED (AHL) family gene in Ascochyta blight resistance of chickpea. Plant Cell Environ. 2018; 41:2128-40

[272] Alo F, Rani AR, Baum M. et al. Novel genomic regions linked to Ascochyta blight resistance in two differentially resistant cultivars of chickpea. Front Plant Sci. 2022; 13:762002

[273] Singh R, Kumar K, Purayannur S. et al. Genomics-assisted genet-ics of complex regions from chickpea chromosome 4 reveals two candidate genes for Ascochyta blight resistance. Plant Sci. 2023; 334:111781

[274] Jain S, Weeden NF, Kumar A. et al. Functional codominant marker for selecting the Fw gene conferring resistance to Fusar-ium wilt race 1 in pea. Crop Sci. 2015; 55:2639-46

[275] Mc Phee KE, Inglis DA, Gundersen B. et al. Mapping QTL for Fusarium wilt race 2 partial resistance in pea (Pisum sativum). Plant Breed. 2012; 131:300-6

[276] Deng D, Sun S, Wu W. et al. Fine mapping and identification of a Fusarium wilt resistance gene FwS1 in pea. Theor Appl Genet. 2024; 137:171

[277] Humphry M, ReinstÄDler A, Ivanov S. et al. Durable broad-spectrum powdery mildew resistance in pea er1 plants is con-ferred by natural loss-of-function mutations in PsMLO1. Mol Plant Pathol. 2011; 12:866-78

[278] Iglesias-García R, Rubiales D, Fondevilla S. Penetration resis-tance to Erysiphe pisi in pea mediated by er1 gene is associated with protein cross-linking but not with callose apposition or hypersensitive response. Euphytica. 2015; 201:381-7

[279] Jha AB, Gali KK, Alam Z. et al. Potential application of genomic technologies in breeding for fungal and oomycete disease resis-tance in pea. Agronomy. 2021; 11:1260

[280] Leprévost T, Boutet G, Lesné A. et al. Advanced backcross QTL analysis and comparative mapping with RIL QTL studies and GWAS provide an overview of QTL and marker haplotype diversity for resistance to Aphanomyces root rot in pea (Pisum sativum). Front Plant Sci. 2023; 14:1189289

[281] Kälin C, Piombo E, Bourras S. et al. Transcriptomic analysis identifies candidate genes for Aphanomyces root rot disease resistance in pea. BMC Plant Biol. 2024; 24:144

[282] Singh C, Kumar R, Sehgal H. et al. Unclasping potentials of genomics and gene editing in chickpea to fight climate change and global hunger threat. Front Genet. 2023; 14:1085024

[283] Hnatuszko-Konka K, Kowalczyk T, Gerszberg A. et al. Phaseolus vulgaris—recalcitrant potential. Biotechnol Adv. 2014; 32:1205-15

[284] Ma X, Zhu Q, Chen Y. et al. CRISPR/Cas9 platforms for genome editing in plants: developments and applications. Mol Plant. 2016; 9:961-74

[285] Li C, Chu W, Gill RA. et al. Computational tools and resources for CRISPR/Cas genome editing. Genomics Proteomics Bioinformatics. 2023; 21:108-26

[286] Ji J, Zhang C, Sun Z. et al. Genome editing in cowpea Vigna unguiculata using CRISPR-Cas9. Int J Mol Sci. 2019; 20:2471

[287] Jurani ć M, Nagahatenna DSK, Salinas-Gamboa R. et al. A detached leaf assay for testing transient gene expression and gene editing in cowpea (Vigna unguiculata [L.] Walp.). Plant Meth-ods. 2020; 16:88

[288] Fernando A, Selvaraj M, Chavarriaga P. et al. A clearinghouse for genome-edited crops and field testing. Mol Plant. 2021; 14:3-5

[289] Badhan S, Ball AS, Mantri N.First report of CRISPR/Cas9 medi-ated DNA-free editing of 4CL and RVE7 genes in chickpea protoplasts. Int J Mol Sci. 2021; 22:396

[290] Che P, Chang S, Simon MK. et al. Developing a rapid and highly efficient cowpea regeneration, transformation and genome editing system using embryonic axis explants. Plant J. 2021; 106: 817-30

[291] Talakayala A, Mekala GK, Reddy MK. et al. Manipulating resis-tance to mungbean yellow mosaic virus in greengram (Vigna radiata L): through CRISPR/Cas9 mediated editing of the viral genome. Front Sustain Food Syst. 2022; 6:911574

[292] Pandey PK, Bhowmik P, Kagale S. Optimized methods for ran-dom and targeted mutagenesis in field pea (Pisum sativum L.). Front Plant Sci. 2022; 13:995542

[293] Voß L, Heinemann KJ, Herde M. et al. Enzymes and cellular interplay required for flux of fixed nitrogen to ureides in bean nodules. Nat Commun. 2022; 13:5331

[294] Li G, Liu R, Xu R. et al. Development of an Agrobacterium-mediated CRISPR/Cas9 system in pea (Pisum sativum L.). Crop J. 2023; 11:132-9

[295] Bhowmik P, Yan W, Hodgins C. et al. CRISPR/Cas9-mediated lipoxygenase gene-editing in yellow pea leads to major changes in fatty acid and flavor profiles. Front Plant Sci. 2023; 14: 1246905

[296] Bridgeland A, Biswas S, Tsakirpaloglou N. et al. Optimization of gene editing in cowpea through protoplast transformation and agroinfiltration by targeting the phytoene desaturase gene. PLoS One. 2023; 18:e0283837

[297] de Koning R, Daryanavard H, Garmyn J. et al. Fine-tuning CRISPR/Cas9 gene editing in common bean (Phaseolus vulgaris L.) using a hairy root transformation system and in silico pre-diction models. Front Plant Sci. 2023; 14:1233418

[298] Wu X, Zhang P, Chen S. et al. A molecular toolkit to boost functional genomic studies in transformation-recalcitrant veg-etable legumes. Hortic Res. 2023;10:uhad064

[299] Tripathi K, Gore PG, Singh MF. et al. Legume genetic resources:status and opportunities for sustainability. In: Hasanuzza-man M, Inte-chOpen:London UK;ed. Legume Crops - Prospects, Production and Uses. 2020:1-10

[300] Santos C, Martins DC, González-Bernal MJ. et al. Integrat-ing phenotypic and gene expression linkage mapping to dissect rust resistance in chickling pea. Front Plant Sci. 2022; 13:837613

[301] Furci L, Jain R, Stassen J. et al. Identification and character-isation of hypomethylated DNA loci controlling quantitative resistance in Arabidopsis. elife. 2019; 8:e40655

[302] Pankaj R, Shoejaeyfar S, Figueiredo DD. An epiQTL underlying asexual seed formation in Arabidopsis. Plant Reprod. 2024; 37: 463-8

[303] Schmitz RJ, He Y, Valdés-López O. et al. Epigenome-wide inheri-tance of cytosine methylation variants in a recombinant inbred population. Genome Res. 2013; 23:1663-74

[304] Satgé C, Moreau S, Sallet E. et al. Reprogramming of DNA methylation is critical for nodule development in Medicago truncatula. Nat Plants. 2016; 2:16166

[305] Niyikiza D, Piya S, Routray P. et al. Interactions of gene expres-sion, alternative splicing, and DNA methylation in determining nodule identity. Plant J. 2020; 103:1744-66

[306] Badon IW, Oh Y, Kim H-J. et al. Recent application of CRISPR-Cas12 and OMEGA system for genome editing. Mol Ther. 2024; 32:32-43

[307] Lv P, Su F, Chen F. et al. Genome editing in rice using CRISPR/Cas12i3. Plant Biotechnol J. 2024; 22:379-85

[308] Wang W, Yan L, Li J. et al. Engineering a robust Cas12i3 variant-mediated wheat genome editing system. Plant Biotechnol J. 2025; 23:860-73

[309] Wang L, O’Conner S, Tanvir R. et al. CRISPR/Cas9-based editing of NF-YC4 promoters yields high-protein rice and soybean. New Phytol. 2024; 245:2103-16